Watch a model go from data to decision.
This is the same pipeline behind every model we ship. Press play, or step through it yourself.
Ingest
Raw data streams in from your existing systems — CRM records, product logs, support tickets — validated and structured in real time.
Train
Models iterate epoch by epoch against your objective, converging on the version that performs best under cross-validation.
Evaluate
Every candidate model is stress-tested against held-out data and edge cases before it ever touches production traffic.
Deploy
Shipped behind a versioned API, monitored continuously, and rolled back automatically the moment drift is detected.
Most "AI projects" never leave the notebook. We build for production from day one.
Off-the-shelf tools can produce an impressive demo in an afternoon — and that's usually where AI product development stalls. The gap between a working prototype and a system your customers can depend on is where most initiatives quietly die: no versioned training data, no evaluation harness, no plan for what happens when the input distribution shifts six months after launch.
Mindwrack exists to close that gap. We treat machine learning and LLM systems as production software: reproducible pipelines, automated testing against real edge cases, staged rollouts, and monitoring that tells you the moment a model's behavior drifts — not a quarter later when a customer notices first. The result is AI product development that ships on a timeline your board can plan around.
Six disciplines, one accountable team
AI product development spans strategy, engineering, infrastructure, and governance — and most teams end up stitching together separate vendors for each. Mindwrack runs all six under one roadmap, so nothing falls in the gap between "the model works" and "the model is live, monitored, and compliant."
AI Product Strategy
Turning a rough idea into a scoped, fundable roadmap.
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We run discovery sprints that pressure-test the business case for an AI feature before a line of code is written — sizing the data you actually have, the accuracy you actually need, and the ROI that justifies the build.
Applied ML Engineering
Models that hold up outside the notebook.
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Our engineers take a model from prototype to production-grade service: reproducible training pipelines, versioned datasets, automated evaluation, and rollback plans for when the world drifts.
Data Platform & MLOps
The plumbing that makes AI reliable, not lucky.
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We design ingestion, feature stores, and CI/CD for models the same way we would for any critical system — with monitoring, alerting, and cost controls built in from day one.
LLM & Agent Systems
Assistants and agents that are grounded, not gimmicky.
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From retrieval-augmented copilots to multi-step autonomous agents, we build LLM systems with evaluation harnesses and guardrails so behavior stays predictable as usage scales.
Cloud-Native Delivery
Infrastructure sized for real traffic, not demo day.
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We architect on AWS, GCP, or Azure with autoscaling inference, cost-aware GPU scheduling, and infrastructure-as-code, so the system you launch with is the one you can still afford at scale.
Responsible AI & Governance
Compliance and fairness reviews, not an afterthought.
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Every engagement includes bias testing, data-privacy review, and documentation aligned to frameworks like the EU AI Act and NIST AI RMF, so legal and security sign off without last-minute surprises.
Numbers our clients report back to their boards
Insights on building AI products that ship
Notes from inside our engagements — evaluation strategies, MLOps patterns, and the tradeoffs behind real production launches, written by the engineers who did the work.
Our first field notes are still in orbit
The engineering team is writing up real lessons from live engagements — evaluation strategies, MLOps patterns, the tradeoffs nobody puts in the sales deck. Check back soon.
See what we've already shippedCase studies from teams who shipped
Every engagement is scoped around a number a client's board actually cares about. Here's what a few of them reported back after launch.
Get a quote in three quick steps
No sales call required to get started — tell us what you're building, who to reach you at, and a bit about the project. We'll follow up with a scoped estimate, not a generic sales deck.
What clients ask before kicking off
What does an AI product development company actually deliver?
A working system, not just a proof of concept: a trained and evaluated model, the data pipeline that feeds it, the API or interface that serves it, and the monitoring that keeps it healthy after launch. We scope engagements around a measurable outcome — a conversion lift, a cost reduction, a response-time target — rather than a research deliverable.
How long does it take to build and ship an AI feature?
Most first production models ship in 8–14 weeks from kickoff, covering data audit, model development, evaluation, and a staged rollout. Simple automation or LLM-copilot features can move faster; systems requiring new data infrastructure or regulatory review take longer. We give a dated plan at the end of discovery, not a rough estimate.
Do you work with our existing data and cloud stack, or require a rebuild?
We build on what you already run — AWS, GCP, Azure, or on-prem — and integrate with your existing warehouse, CRM, or event pipeline wherever possible. A rebuild is only recommended when the current stack genuinely cannot support the reliability or scale the product needs.
How do you handle AI model accuracy, bias, and compliance requirements?
Every model ships with a documented evaluation suite covering accuracy, fairness across relevant subgroups, and edge-case behavior, plus a data-privacy review mapped to your regulatory context (GDPR, HIPAA, the EU AI Act, or industry-specific rules). This documentation is a deliverable, not an internal artifact — your legal and security teams get it directly.
What happens after launch — do you support the model long-term?
Yes. We offer ongoing MLOps retainers that cover drift monitoring, scheduled retraining, incident response, and cost optimization, or we can hand off a fully documented system to your internal team with a defined transition period either way.
Building AI products for SaaS.
Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool for it.
Get a quote